Abstract
The currently burst of the Internet of Things (IoT) technologies implies the emergence of new lines of investigation regarding not only to hardware and protocols but also to new methods of produced data analysis satisfying the IoT environment constraints: a real-time and a big data approach. The Real-time restriction is about the continuous generation of data provided by the endpoints connected to an IoT network; due to the connection and scaling capabilities of an IoT network, the amount of data to process is so high that Big data techniques become essential. In this article, we present a system consisting of two main modules. In one hand, the infrastructure, a complete LoRa based network designed, tested and deployment in the Pablo de Olavide University and, on the other side, the analytics, a big data streaming system that processes the inputs produced by the network to obtain useful, valid and hidden information.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akgün, B., Öǧüdücü, S.G.: Streaming linear regression on Spark MLlib and MOA. In: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 1244–1247 (2015)
Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mob. Netw. Appl. 19(2), 171–209 (2014)
D’Silva, G.M., Khan, A., Gaurav, Bari, S.: Real-time processing of IoT events with historic data using Apache Kafka and Apache Spark with dashing framework. In: Proceedings of the IEEE International Conference on Recent Trends in Electronics, Information Communication Technology, pp. 1804–1809 (2017)
Galicia, A., Talavera-Llames, R., Troncoso, A., Koprinska, I., Martínez-Álvarez, F.: Multi-step forecasting for big data time series based on ensemble learning. Knowl. Based-Syst. 163, 830–841 (2019)
Gutiérrez-Avilés, D., Fábregas, J.A., Tejedor, J., Martínez-Álvarez, F., Troncoso, A., Arcos, A., Riquelme, J.C.: SmartFD: a real big data application for electrical fraud detection. Lect. Notes Artif. Intell. 10870, 120–130 (2018)
Gutiérrez-Avilés, D., Rubio-Escudero, C., Martínez-Álvarez, F., Riquelme, J.: Trigen: a genetic algorithm to mine triclusters in temporal gene expression data. Neurocomputing 132, 42–53 (2014)
Gutiérrez-Avilés, D., Giráldez, R., Gil-Cumbreras, F.J., Rubio-Escudero, C.: TRIQ: a new method to evaluate triclusters. BioData Min. 11, id15 (2018)
Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)
Hu, T., Wu, Q., Zhou, D.X.: Convergence of gradient descent for minimum error entropy principle in linear regression. IEEE Trans. Signal Process. 64(24), 6571–6579 (2016)
Ichinose, A., Takefusa, A., Nakada, H., Oguchi, M.: A study of a video analysis framework using Kafka and Spark Streaming. In: Proceedings of the IEEE International Conference on Big Data, pp. 2396–2401 (2017)
Karakaya, Z., Yazici, A., Alayyoub, M.: A comparison of stream processing frameworks. In: Proceedings of the International Conference on Computer and Applications, pp. 1–12 (2017)
Lasi, H., Fettke, P., Kemper, H.G., Feld, T., Hoffmann, M.: Industry 4.0. Bus. Inf. Syst. Eng. 6(4), 239–242 (2014)
Lauridsen, M., Nguyen, H., Vejlgaard, B., Kovacs, I.Z., Mogensen, P., Sorensen, M.: Coverage Comparison of GPRS, NB-IoT, LoRa, and SigFox in a 7800 km\(^{2}\) Area. In: Proceedings of the IEEE Vehicular Technology Conference, pp. 1–5 (2017)
Marz, N., Warren, J.: Big Data: Principles and Best Practices of Scalable Real-time Data Systems. Manning Publications Co., Shelter Island (2015)
Noac’h, P.L., Costan, A., Bougé, L.: A performance evaluation of Apache Kafka in support of big data streaming applications. In: Proceedings of the IEEE International Conference on Big Data, pp. 4803–4806 (2017)
Pallaprolu, S.C., Sankineni, R., Thevar, M., Karabatis, G., Wang, J.: Zero-day attack identification in streaming data using semantics and Spark. In: Proceedings of the IEEE International Congress on Big Data, pp. 121–128 (2017)
Rahman, A., Suryanegara, M.: The development of IoT LoRa: a performance evaluation on LoS and Non-LoS environment at 915 MHz ISM frequency. In: Proceedings of the International Conference on Signals and Systems, pp. 163–167 (2017)
Rizzi, M., Ferrari, P., Flammini, A., Sisinni, E.: Evaluation of the IoT LoRaWAN solution for distributed measurement applications. IEEE Trans. Instrum. Meas. 66(12), 3340–3349 (2017)
Shvachko, K., Kuang, H., Radia, S., Chansler, R.: The Hadoop distributed file system. In: Proceedings of the IEEE Symposium on Mass Storage Systems and Technologies, pp. 1–10 (2010)
Torres, J.F., Galicia, A., Troncoso, A., Martínez-Álvarez, F.: A scalable approach based on deep learning for big data time series forecasting. Integr. Comput.-Aided Eng. 25(4), 335–348 (2018)
Wortmann, F., Flüchter, K.: Internet of things. Bus. Inf. Syst. Eng. 57(3), 221–224 (2015)
Acknowledgments
We would like to thank the Spanish Ministry of Economy and Competitiveness for the support under project TIN2017-88209-C2-1-R. Additionally, we want to express our gratitude to Enrique Parrilla, Lantia IoT’s CEO, since all the equipment has been provided by him. The T-Systems Iberia company is also acknowledged since all experiments have been carried out on its Open Telekom Cloud Platform based on the OpenStack open source.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Fernández, A.M., Gutiérrez-Avilés, D., Troncoso, A., Martínez-Álvarez, F. (2020). Real-Time Big Data Analytics in Smart Cities from LoRa-Based IoT Networks. In: Martínez Álvarez, F., Troncoso Lora, A., Sáez Muñoz, J., Quintián, H., Corchado, E. (eds) 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019). SOCO 2019. Advances in Intelligent Systems and Computing, vol 950. Springer, Cham. https://doi.org/10.1007/978-3-030-20055-8_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-20055-8_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20054-1
Online ISBN: 978-3-030-20055-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)